Rainfall vs Niño#
https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/doc/GHCND_documentation.pdf
import warnings
warnings.filterwarnings("ignore")
import os
import sys
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import df2img
sys.path.append("../../../../indicators_setup")
from ind_setup.colors import get_df_col, plotting_style
from ind_setup.tables import plot_df_table
from ind_setup.plotting_int import plot_oni_index_th
from ind_setup.plotting import plot_bar_probs_ONI
plotting_style()
from ind_setup.core import fontsize
sys.path.append("../../../functions")
from data_downloaders import GHCN, download_oni_index
Define location and variables of interest#
country = 'Palau'
vars_interest = ['PRCP']
Get Data#
df_country = GHCN.get_country_code(country)
print(f'The GHCN code for {country} is {df_country["Code"].values[0]}')
The GHCN code for Palau is PS
df_stations = GHCN.download_stations_info()
df_country_stations = df_stations[df_stations['ID'].str.startswith(df_country.Code.values[0])]
print(f'There are {df_country_stations.shape[0]} stations in {country}')
There are 13 stations in Palau
GHCND_dir = 'https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/access/'
Using Koror Station#
id = 'PSW00040309' # Koror Station
dict_prcp = GHCN.extract_dict_data_var(GHCND_dir, 'PRCP', df_country_stations.loc[df_country_stations['ID'] == id])[0]
st_data = dict_prcp[0]['data'].dropna()
ONI index#
https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php
p_data = 'https://psl.noaa.gov/data/correlation/oni.data'
df1 = download_oni_index(p_data)
lims = [-.5, .5]
plot_oni_index_th(df1, lims = lims)
st_data_monthly = st_data.resample('M').mean()
st_data_monthly.index = pd.DatetimeIndex(st_data_monthly.index).to_period('M').to_timestamp() + pd.offsets.MonthBegin(1)
st_data_monthly
| PRCP | |
|---|---|
| DATE | |
| 1951-08-01 | 7.800000 |
| 1951-09-01 | 16.800000 |
| 1951-10-01 | 7.266667 |
| 1951-11-01 | 5.554839 |
| 1951-12-01 | 9.093333 |
| ... | ... |
| 2024-10-01 | 13.756667 |
| 2024-11-01 | 7.055172 |
| 2024-12-01 | 7.751852 |
| 2025-01-01 | 17.175000 |
| 2025-02-01 | 6.350000 |
883 rows × 1 columns
rolling_mean = 6 #months
df1['prcp'] = st_data_monthly['PRCP'].rolling(window=rolling_mean).mean()
fig, ax = plt.subplots(figsize = [15, 6])
df1.ONI.plot(ax = ax, color = get_df_col()[0], lw = 2)
ax2 = ax.twinx()
df1.prcp.plot(ax = ax2, color = get_df_col()[1], lw = 2)
# df1.tmax.plot(ax = ax2, color = get_df_col()[1], lw = 2)
# df1.tdiff.plot(ax = ax2, color = get_df_col()[1], lw = 2)
# df1.tmean.plot(ax = ax2, color = get_df_col()[1], lw = 2)
<Axes: >
low_lim = np.nanmin(df1.prcp)
fig, ax = plt.subplots(figsize = [15, 6])
df1.prcp.plot(ax = ax, color = get_df_col()[1], lw = 2)
ax.fill_between(df1.index, low_lim, df1.prcp, where = (df1.ONI > lims[1]), color = 'lightcoral',
alpha = 0.7, label = f'ONI over th: {lims[1]}')
ax.fill_between(df1.index, low_lim, df1.prcp, where = (df1.ONI < lims[0]), color = 'lightblue',
alpha = 0.7, label = f'ONI below th: {lims[0]}')
ax.fill_between(df1.index, low_lim, df1.prcp, where = ((df1.ONI > lims[0]) & (df1.ONI < lims[1])),
color = 'lightgrey', alpha = 0.075)
ax.legend(fontsize=fontsize)
ax.set_title('PRECIPITATION and ONI', fontsize = fontsize)
ax.set_ylabel('PRECIP', fontsize = fontsize)
ax.set_xlabel('Time', fontsize = fontsize)
Text(0.5, 0, 'Time')
plt.figure(figsize=(5, 4))
sns.heatmap(df1.corr(), annot=True, cmap='coolwarm', vmin=-1, vmax=1)
plt.title('Correlation Heatmap')
plt.show()
df2 = df1.resample('Y').mean()
df2['oni_cat'] = np.where(df2.ONI > lims[1], 1, np.where(df2.ONI < lims[0], -1, 0))
fig = plot_bar_probs_ONI(df2, var='prcp')
df2['prcp_ref'] = df2.prcp - df2.loc['1961':'1990'].prcp.mean()
fig = plot_bar_probs_ONI(df2, var='prcp_ref')
fig.suptitle('Precipitation Anomaly over the 1961-1990 mean', fontsize = fontsize)
Text(0.5, 0.98, 'Precipitation Anomaly over the 1961-1990 mean')
df_format = np.round(df1.describe(), 2)
fig = plot_df_table(df_format, figsize = (400, 300))
df2img.save_dataframe(fig=fig, filename="getting_started.png")
